Some computational and modeling issues for hierarchical models

How can we fit a complex statistical model and have confidence in our results? There are several challenges, including (a) setting up models that are complicated enough to reflect the aspects of reality that we want to study, (b) regularization or partial pooling to get stable estimates for the resulting large number of parameters, (c) actually fitting the model (in Bayesian terms, getting a point estimate or posterior simulations, (d) checking the fit of the model to data, (e) attaining confidence that the fitting procedure is bug-free, and (f) understanding the fitted model. We discuss these in the context of nonnested varying-intercept, varying-slope multilevel logistic regression models that we have been using to estimate public opinion in demographic and geographic subgroups of the U.S. population.

Mon 26 Oct, 9.30 on the ground floor of the Latarjet building at International Agency for Research on Cancer (IARC), 150 Cours Albert Thomas, Lyon. This is where Martyn Plummer (the JAGS guy) works.